modeling cross-contamination in quantitative microbial risk assessment
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Modeling Cross-contamination in Quantitative Microbial Risk Assessment. Don Schaffner Food Risk Analysis Initiative Rutgers University. The Achilles heel of risk assessment - G. Paoli 7/24/02. Modeling Cross-contamination in Quantitative Microbial Risk Assessment. - PowerPoint PPT PresentationTRANSCRIPT
1st International Conference on Microbial Risk Assessment: Foodborne Hazards, College Park MD, July 2002
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Modeling Cross-contamination in
Quantitative Microbial Risk Assessment
Don SchaffnerFood Risk Analysis Initiative
Rutgers University
1st International Conference on Microbial Risk Assessment: Foodborne Hazards, College Park MD, July 2002
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Modeling Cross-contamination in
Quantitative Microbial Risk Assessment
Don SchaffnerFood Risk Analysis Initiative
Rutgers University The Achilles heel of risk assessment- G. Paoli 7/24/02
1st International Conference on Microbial Risk Assessment: Foodborne Hazards, College Park MD, July 2002
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Laboratory Experiments
• Nalidixic acid resistant Enterobacter aerogenes with attachment characteristics similar to Salmonella
• More than 30 participants dice inoculated chicken, wash hands and/or wear gloves, slice lettuce
• Sample hands, foods, faucet spigots cutting boards for Enterobacter
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Why these studies ?
• Practical consideration– A company was interested in
showing efficacy of a touch-free faucet… they provided funding!
• Our research philosophy– Variability matters, especially for
modeling and risk assessment
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Things to think about…
• A surface can either …– be sampled or – be used to contaminate another surface
• Relative numbers and rates– Dirty hands -> clean faucet handles– Dirty hands <-> dirty faucet handles– Clean hands <- dirty faucet handles
• How many observations at one set of conditions are needed?
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Data Analysis
• Log transformation of % transfer• Frequency histogram in Excel• Best distribution using BestFit• Normal distributions fit the data
100Hand on CFU
Spigot on CFU(%) rate Transfer
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Think about data transformation…
Percent Transfer
0 25 50 75 100
Fre
quen
cy
0
2
4
6
8
Log Percent Transfer
-2 -1 0 1 2
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Cross contamination results
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Glove barrier: Chicken to hand
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Our Published Work
• Chen, Y., Jackson, K.M. Chea, F.P. and Schaffner, D.W. 2001. Quantification and variability analysis of bacterial cross-contamination rates in the kitchen. Journal of Food Protection. 64(1):72-80.
• Montville, R., Chen, Y., and Schaffner, D.W. 2001. Glove barriers to bacterial cross-contamination. Journal of Food Protection. 64(6), 845–849.
• Montville, R., Chen, Y. and Schaffner, D.W., 2002. Risk assessment of handwashing efficacy using literature and experimental data. International Journal of Food Microbiology 73(2-3), 305-313.
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I nvestigator Organization Project
Griffi th University of Wales Behavior f requencies, high risk surf aces
J aykus North Carolina State University
Cross contamination with viruses and pathogenic bacteria
Kasuga National I nstitute of I nf ectious Diseases
Cross contamination with naturally occurring bacteria, diff erent f oods
Mattick University of Bristol Dishwashing eff ectiveness
Michaels Georgia Pacifi c Handwashing and cross contamination
Sobsey Univ NC, Chapel Hill Simultaneous Serratia and Phage transf er
Todd Michigan State Cross contamination with Listeria
Currently ongoing research with application on microbial behavior in the kitchen environment
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Other recent publications
• L. L. Gibson, J. B. Rose, C. N. Haas, C. P. Gerba, and P. A. Rusin. Quantitative assessment of risk reduction from hand washing with antibacterial soaps. J.Appl.Microbiol. 92:136S-143S, 2002.– “The objective of this study was to examine the risk reduction
achieved from using different soap formulations after diaper changing using a microbial quantitative risk assessment approach.”
• T. A. Cogan, J. Slader, S. F. Bloomfield, and T. J. Humphrey. Achieving hygiene in the domestic kitchen: the effectiveness of commonly used cleaning procedures. J.Appl.Microbiol. 92 (5):885-892, 2002.– “Aims: To quantify the transmission of Salmonella and Campylobacter
to hands, cloths, and hand- and food-contact surfaces during the preparation of raw poultry in domestic kitchens, and to examine the impact on numbers of these bacteria of detergent-based cleaning alone, or in conjunction with thorough rising.”
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Things to consider in QMRA
• Cross-contamination must be handled differently than other increases– Two log increase due to growth: 1 + 2 =
3– 100 CFU added from cross-
contamination: 10 + 100 = 110
• Modeling the non-linear nature of the process
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Non-linear process
Raw chickenInitial Cooked chicken
Cutting board
Hand
Lettuce
Exposure
Storage effectlog increase
Xcontam rateraw to board
Cooking effectlog decrease
Xcontam ratehand to mouth
Xcontam rateboard to lettuce
Xcontam rateraw to hand
Xcontam ratehand to cooked
Xcontam ratehand to lettuce
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Model interfaceModel
Initial 100
Storage effect log increase 2
Cooking effect log decrease 5
Log Exposure Calc
Log exposure stats Calc
Xratemodule
xrate mean -2
xrate sd 1
Exp where less than 1 is zero Calc
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High Rate Resultsinitial: 1000storage increase: 1cooking decrease: 5log cross contamination rate mean: -1 (10%)log cross contamination standard deviation: 1
Pro
bab
ilit
y D
ensi
ty
Log Exposure-2.5 0 2.5 5 7.5 10
0
0.5
1
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Low Rate Results
initial: 1000storage increase: 1cooking decrease: 5log cross contamination rate mean: -3 (0.1%)log cross contamination standard deviation: 1
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Where to we go from here?
• What factors are important in controlling transfer rate?– Soil type, organism, pressure, concentration, etc.
• What routes are important?– Hand to mouth, cutting board to raw product, etc.
• What behaviors are important?– Handwashing, cleaning frequency, etc.
• Once we know what’s important, we can ignore what’s not important, and include a useful, simplified cross contamination module in our risk assessments
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Acknowledgements
• Lee Budd for stimulating discussions• Sloan Valve company for support and
funding• The Food Risk Analysis Initiative was
funded in part by the New Jersey Agricultural Experiment Station
• Members of the FoRAI team: Yuhuan Chen, Rebecca Montville, Kristin Jackson, Siobain Duffy, Purvi Vora, Lihui Zhao